A new algorithm for estimating association parameters in molecular-based equations of state by quantum chemistry

Lucia, Ángelo; Octavio, Leah M.; Visco, Donald P.

doi:10.1016/j.compchemeng.2008.10.015

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…In addition, highly accurate quantum calculations on small molecules can be used as training data to improve the accuracy of empirical methods that enable fast property prediction relative to direct quantum mechanical methods. Potential applications include parameterizing equations of state, 50 calibrating force fields for molecular dynamics simulation, 51 and development of efficient property estimation algorithms for computer-aided molecular design. 52 To determine the thermodynamic properties of a molecule of interest (or kinetic rate parameters for a reaction), many steps beyond computing the electronic energy are required.…”

Section: Computational Chemistry and Molecular Simulationmentioning

confidence: 99%

Perspectives of quantum computing for chemical engineering

et al. 2022

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Quantum computing has been attracting public attention recently. This interest is driven by the advancements in hardware, software, and algorithms required for its successful usage and the promise that it entails the potential acceleration of computational tasks compared to classical computing. This perspective article presents a short review on quantum computing, how this computational approach solves problems, and three fields that quantum computing can potentially impact the most while relevant to chemical engineering: computational chemistry, optimization, and machine learning. Here, we present a series of chemical engineering applications, the developments, potential improvements with respect to classical computing, and challenges that quantum computing faces for each of these fields. This article intends to provide a clear picture of the challenges and potential advantages that quantum technology may yield for chemical engineering, together with an invitation for our colleagues to join us in the adoption and development of quantum computing.

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Section: Computational Chemistry and Molecular Simulationmentioning

confidence: 99%

Perspectives of quantum computing for chemical engineering

et al. 2022

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“…While the correlations of the pure-component properties can be outstanding, lack of sufficient experimental data acts as a limitation to fitting additional pure-component parameters of new species. Prior works have shown that these parameters can be correlated to molecular properties such as molecular weight. , Some also propose approaches to predict the pure-component properties based on molecular simulation, critical properties/acentric factors, − information from quantum-chemistry calculations, − or machine learning. − …”

Section: Introductionmentioning

confidence: 99%

Confidence-Interval and Uncertainty-Propagation Analysis of SAFT-type Equations of State

Walker,

Mueller,

Smirnova

2023

J. Chem. Eng. Data

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Thermodynamic models and, in particular, Statistical Associating Fluid Theory (SAFT)-type equations, are vital in characterizing complex systems. This paper presents a framework for sampling parameter distributions in PC-SAFT and SAFT-VR Mie equations of state to examine parameter confidence intervals and correlations. Comparing the equations of state, we find that additional parameters introduced in the SAFT-VR Mie equation increase relative uncertainties (1%−2% to 3%−4%) and introduce more correlations. These correlations can be attributed to conserved quantities such as particle volume and interaction energy. When incorporating association through additional parameters, relative uncertainties increase further while slightly reducing correlations between parameters. We also investigate how uncertainties in parameters propagate to the predicted properties from these equations of state. While the uncertainties for the regressed properties remain small, when extrapolating to new properties, uncertainties can become significant. This is particularly true near the critical point where we observe that properties dependent on the isothermal compressibility observe massive divergences in the uncertainty. We find that these divergences are intrinsic to these equations of state and, as a result, will always be present regardless of how small the parameter uncertainties are.

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